A network analysis of self-compassion and burnout in a sample of nurses

The role of self-care and self-compassion in networks of resilience and stress among healthcare professionals

Healthcare professionals (HCPs) are essential for maintaining our healthcare system but are at risk for developing mental health issues due to chronic occupational stress. This can lead to a vicious cycle with extended sick leave, increased workloads for colleagues, and strain on the healthcare system. Therefore, preventive interventions aiming at enhancing resilience - the maintenance of mental health despite stress - are essential. Yet, identifying the most impactful resilience factors has been challenging. To explore the relationships between resilience factors, stress, mental health, and work-related outcomes, we conducted regularized partial correlation network analyses focusing on self-care and self-compassion. Cross-sectional data from HCPs in Germany were collected from June-July 2023. Analyses of 212 HCPs (age 41.63 [21-68] years; 81.60% women) revealed self-compassion as the most important factor across all networks, while the importance of self-care showed through individual connections to crucial factors like mental health problems and work-life balance. Work engagement, contrary to burnout, was closely interrelated with resilience factors. In conclusion, despite accounting for established evidence-based resilience factors, self-compassion and self-care seem crucial in the context of stress and mental health in HCPs. More research is needed to validate the causal importance of self-care and self-compassion.

The network analysis of learning burnout and sleep quality among medical students during online learning in China

Previous studies had identified the significant issue of burnout and sleep quality in medical students. However, no studies have explored the interactions between learning burnout and sleep quality on a symptom level. This study used network analysis to explore the interaction and construct the network structure of learning burnout and sleep quality among medical students in China. We recruited 553 medical students to participate in our study. Learning Burnout of Undergraduates and Scale (LBUS) and the Pittsburgh Sleep Quality Index (PSQI) were used to measure learning burnout and sleep quality. Expected influence and bridge expected influence were used to identify the central and bridge symptoms. Results showed 'B9' (Tired of learning) and 'B17' (I want to learn but feel bored with it) had the highest expected influence. 'B12' (I often fall asleep while studying) and 'P_DD' (Daytime dysfunction) had the highest bridge expected influence. Our findings revealed the characteristics of learning burnout and sleep quality in online learning and provided information to further understand the difference in the influence of mental health between online and offline learning.

Network analysis of resilience, anxiety and depression in clinical nurses

BACKGROUND

Resilience is a protective feature against anxiety and depression disorders. However, the precise relationship and structure of resilience and anxiety and depression remain poorly understood. This study sought to investigate the link among resilience' components and anxiety as well as depression.

METHODS

1,279 clinical nurses were recruited. 10-item Connor-Davidson Resilience Scale, Generalized Anxiety Disorder 7, and Patient Health Questionnaire 9 were employed to evaluate resilience, anxiety, and depression, respectively. The regularized partial-correlation network was generated utilizing data from cross-sectional survey and the bridge expected influence index was utilized to quantify bridge components.

RESULTS

The rates of anxiety and depression within clinical nurses were 67.3% and 67.2%, accordingly. Four strongest bridge edges appeared in the resilience-anxiety network, like "Adapt to change"- "Fear that something might happen", and "Stay focused under pressure"- "Uncontrollable worry". Two strongest bridge edges appeared in the resilience-depression network, like "Adapt to change"- "Concentration difficulties" and "Stay focused under pressure"- "Fatigue". "Adapt to change" was recognized as bridging nodes in both the resilience-anxiety network and the resilience-depression network.

CONCLUSIONS

Interventions targeting the bridge component "Adapt to change" within resilience, may mitigate the intensity of anxiety and depression symptoms among clinical nurses.